View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Harper Adams University Repository Phylogenetic clustering of wingbeat frequency and flight-associated morphometrics across insect orders by Tercel, M.P.T.G., Veronesi, F. and Pope, T.W. Copyright, Publisher and Additional Information: This is the author accepted manuscript. The final published version (version of record) is available online via Wiley. This article may be used for non‐commercial purposes in accordance with Wiley Terms and Conditions for Self‐Archiving. Please refer to any applicable terms of use of the publisher. DOI: https://doi.org/10.1111/phen.12240 Tercel, M.P.T.G., Veronesi, F. and Pope, T.W. 2018. Phylogenetic clustering of wingbeat frequency and flight‐associated morphometrics across insect orders. Physiological Entomology. 24 March 2018 1 Title 2 Phylogenetic clustering of wingbeat frequency and flight-associated morphometrics across 3 insect orders 4 5 Running title 6 Flight strategies between insect orders 7 8 Authors 9 Maximillian P. T. G. Tercel*, Fabio Veronesi and Tom W. Pope 10 11 Affiliation 12 Department of Crop and Environment Science, Harper Adams University, Newport, 13 Shropshire, TF10 8NB, UK. 14 15 *Author for correspondence ([email protected]) 16 17 18 KEY WORDS: Comparative morphology, wing loading, body mass, flight strategy, insect 19 evolution, high-speed filming. 20 21 22 23 Summary statement 24 Insect flight strategy varies between orders but is generally well conserved within orders, this 25 has important evolutionary and ecological implications at high taxonomic levels. 26 27 ABSTRACT 28 Wingbeat frequency in insects is an important variable in aerodynamic and energetic 29 analyses of insect flight and has been studied previously on a family- or species-level basis. 30 Meta-analyses of these studies have found order-level patterns that suggests flight strategy 31 is moderately well conserved phylogenetically. Studies incorporated into these meta- 32 analyses, however, use variable methodologies across different temperatures that may 33 confound results and phylogenetic patterns. Here, a high-speed camera was used to 34 measure wingbeat frequency in a wide variety of species (n = 102) in controlled conditions to 35 determine the validity of previous meta-analyses that show phylogenetic clustering of flight 36 strategy and to identify new evolutionary patterns between wingbeat frequency, body mass, 37 wing area, wing length, and wing loading at the order level. All flight-associated 38 morphometrics significantly affected wingbeat frequency. Linear models show that wing area 39 explained the most amount of variation in wingbeat frequency (R2 = 0.59, p = <0.001), whilst 40 body mass explained the least (R2 = 0.09, p = <0.01). A multiple regression model 41 incorporating both body mass and wing area was the best overall predictor of wingbeat 42 frequency (R2 = 0.84, p = <0.001). Order-level phylogenetic patterns across relationships 43 were consistent with previous studies. Thus, the present study provides experimental 44 validation of previous meta-analyses and provides new insights into phylogenetically 45 conserved flight strategies across insect orders. 46 47 48 INTRODUCTION 49 Wingbeat frequency in insects varies with body mass and wing area within and between 50 species (Byrne et al., 1988; Dudley, 2000), from 5.5 Hz in the helicopter damselfly 51 Megaloprepus caerulatus (Rüppell and Fincke, 1989) to over 1000 Hz in a ceratopogonid 52 Forcipomyia sp. midge (Sotavalta, 1953). How frequently an insect beats its wings is an 53 important variable when considering the biomechanics and physiology of insect flight 54 (Ellington, 1984a-f; Dudley, 2000; Alexander, 2002; Vogel, 2013). For any given body mass, 55 variables such as wing length, wing area, wing loading (body mass/wing area), wingbeat 56 frequency and stroke amplitude can differ substantially and affect the energetics and 57 biomechanics of insect flight, which is usually linked to evolutionary history (Byrne et al., 1988). 58 Stroke amplitude, the angle between the points of wing reversal, has been shown to vary 59 between taxa, from 66o in syrphids (Ellington, 1984c) to 180o in beetles (Atkins, 1960) and 60 moths (Wilkins, 1991) and may vary significantly during a single flight as shown in dragonflies 61 (Alexander, 1986), orchid bees (Dudley, 1995; Dillon and Dudley, 2004), and fruit flies 62 (Lehmann and Dickinson, 1998; Fry et al., 2003). Though undeniably important to 63 understanding insect flight strategy and aerodynamics, stroke amplitude was not measured in 64 the current study. This is because although both wingbeat frequency and stroke amplitude 65 change during a single flight, wingbeat frequency is kept relatively constant because of the 66 high energetic cost of deviating from the resonant frequency of the flight apparatus (Dudley, 67 2000). Conversely, stroke amplitude may be altered extremely rapidly to change direction (Fry 68 et al., 2003) or flight mode e.g. from hovering to forward flight (Dillon and Dudley, 2004). 69 Because of this variability, stroke amplitude is likely to be a slightly less reliable indicator of 70 flight strategy than wingbeat frequency. 71 The variables that influence the energetic and biomechanical aspects of flight could be used 72 to broadly characterize flight strategies between different orders of insects. Typically, higher 73 wingbeat frequencies are associated with insects of smaller size, to overcome the increasingly 74 viscous forces of the air present at small spatial scales, represented by low Reynolds numbers 75 Re in the order of 10-100 in the smallest insects (Ellington, 1999; Wang, 2005), and to better 76 control their direction in a windswept world (Vogel, 2013). Furthermore, frequencies of >100 77 Hz are facilitated by asynchronous, or myogenic, flight muscle present in endopterygote 78 (Coleoptera, Diptera, Hymenoptera) and exopterygote (Thysanoptera and Hemiptera) groups 79 (Dudley, 2000) where one nerve impulse can initiate several wingbeats through stretch- 80 activation caused by mechanical loading on the wing (Pringle, 1967). Thus, the highest 81 wingbeat frequencies are found in smaller members of these groups (Byrne et al., 1988). 82 Members from other orders possess large wings that they beat at lower frequencies relative 83 to other insects of comparable body mass e.g. Lepidoptera and Neuroptera (Dudley, 2000) 84 and Orthoptera (Snelling et al., 2012, 2017). Larger wings can produce more force per beat 85 than smaller wings, and therefore fewer beats are needed per unit time. Moreover, larger 86 wings afford lower wing loadings for insects of the same body mass, so wingbeat frequency 87 may be reduced further. It is possible then that flight-associated morphometrics, such as wing 88 area, can be used to predict wingbeat frequency and characterize flight for different groups of 89 insect using the same stroke strategy (i.e. conventional wingbeat or clap-fling). 90 91 Flight morphology and wingbeat frequency are dependent on the aerodynamic needs of the 92 insect according to their ecological niche and oxygen consumption increases with wingbeat 93 frequency (Bartholomew and Casey, 1978). Species with similar wing loadings may have 94 different wingbeat frequencies based on the flight velocity required to fulfil their ecological role. 95 Substantial variation in wingbeat frequency and flight morphology as a product of ecological 96 needs also exists within orders, such as the differences between Sphingidae and Nymphalidae 97 (Lepidoptera), where sphingids have small wings, rapid beat frequencies and very fast flight, 98 whilst nymphalids have much larger wings and lower wingbeat frequencies, usually flying at 99 overall slower speeds (Dudley, 2000). Such variation could conceal relationships between 100 flight-associated morphometrics and wingbeat frequency across higher taxonomic levels, 101 decreasing the overall level of phylogenetic grouping of flight strategy. 102 103 Order-level taxonomic relationships to these flight-associated morphometrics have been 104 studied before (see Byrne et al., 1988; Dudley, 2000) but meta-analyses suffer from 105 differences in both ambient conditions and methods of measuring wingbeat frequency 106 between studies that may confuse relationships. For example, acoustic methods, 107 stroboscopes, and high-speed cameras were used across studies incorporated into Dudley 108 (2000) and Byrne et al.’s (1988) meta-analyses. Chadwick (1939) suggested stroboscopic 109 methods are difficult to use effectively to glean kinematic data in insects because of the slight 110 variations in wingbeat frequency and movements of the specimen during testing, making 111 visualisation of the wing at the frequency of the strobe light challenging and Unwin and 112 Ellington (1979) suggested picking up acoustic signals of smaller species difficult even with 113 highly sensitive microphones. Both stroboscopic (e.g. Chen et al., 2014) and acoustic (e.g. 114 Raman et al., 2007) methods have, however, been used successfully to measure wingbeat 115 frequency in insects since advancement in the quality of measurement instruments (i,e, optical 116 tachometers and microphones). Nevertheless, stroboscopic/optical and acoustic methods are 117 not absolute measures of wingbeat frequency. High-speed cameras, in contrast, allow the 118 recording of a temporally magnified visual depiction of the motion of insect wings. The 119 reliability of the methods used